Simple triage protocol cuts ER wait times without adding beds or staff, new study finds

Emergency departments across the country face a common challenge: how to reduce long wait times without adding more beds, hiring more staff or expanding facilities. New research suggests one answer may be hiding in plain sight—making better decisions about where patients receive care.

A new study published in the journal Management Science, titled “Vertical Patient Streaming in Emergency Departments,” found that a simple, data-driven triage protocol reduced emergency department length of stay by 11 minutes without compromising patient safety or requiring additional resources.

Researchers from Harvard University, Oxford University and Mayo Clinic developed an evidence-based protocol to identify patients who could safely receive care in a seated treatment area—known as a vertical processing pathway—instead of occupying a traditional emergency department bed.

Although many emergency departments already have these areas, decisions about who should be treated there are often made on an ad hoc basis. The researchers sought to replace that variability with a standardized, easy-to-use protocol.

The team first analyzed nearly 50,000 emergency department visits at Mayo Clinic Arizona to develop a machine-learning model that predicts, using only information collected during triage, whether a patient will ultimately require an emergency department bed. They then combined those predictions with mathematical models of patient flow to determine the most efficient routing strategy before translating the results into a straightforward decision tree that clinicians could implement without new software or changes to hospital IT systems.

To evaluate the approach in practice, the researchers conducted a 13-week prospective field trial involving 11,015 patients at Mayo Clinic Arizona’s new emergency department.

The results demonstrated measurable improvements in efficiency:

• 11-minute (4.2%) reduction in total emergency department length of stay.
• Eight-minute (4.5%) reduction in time from arrival to clinical disposition.
• No increase in 72-hour return visits, indicating patient care quality was maintained.

“Our goal wasn’t to add technology to the emergency department,” said Arshya Feizi, lead author of the study and a researcher at Harvard University. “It was to give clinicians a practical, evidence-based way to decide which patients can safely receive care without occupying one of the department’s limited beds.”

The protocol relies only on information hospitals already collect during triage, including a patient’s Emergency Severity Index score, presenting complaint and whether the department is operating over capacity. Because it requires no additional staff, equipment or software integration, the researchers say it could be implemented quickly in many emergency departments.

“Our findings show that improving patient flow doesn’t always require expanding capacity,” said Soroush Saghafian, co-author of the study and professor at Harvard University. “Sometimes the greatest opportunity comes from using existing resources more intelligently.”

The researchers estimate that a medium-sized emergency department treating approximately 40,000 patients annually could recover nearly 6,800 bed-hours each year—enough capacity to care for roughly 2,000 additional patients while potentially generating approximately $3 million in additional reimbursement, all without expanding facilities or increasing staffing.

Emergency department overcrowding has challenged hospitals for decades, contributing to treatment delays, patient dissatisfaction, clinician burnout and higher health care costs. The researchers believe their findings demonstrate that operational improvements grounded in analytics and implemented through simple clinical protocols can produce meaningful gains without sacrificing quality of care.

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Gaby Clark

MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news.

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Andrew Zinin

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